{"title":"Elimination Noise by Adaptive Wavelet Threshold","authors":"Iman Elyasi, Sadegh Zarmehi","volume":32,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":1541,"pagesEnd":1546,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/8882","abstract":"
Due to some reasons, observed images are degraded which are mainly caused by noise. Recently image denoising using the wavelet transform has been attracting much attention. Waveletbased approach provides a particularly useful method for image denoising when the preservation of edges in the scene is of importance because the local adaptivity is based explicitly on the values of the wavelet detail coefficients. In this paper, we propose several methods of noise removal from degraded images with Gaussian noise by using adaptive wavelet threshold (Bayes Shrink, Modified Bayes Shrink and Normal Shrink). The proposed thresholds are simple and adaptive to each subband because the parameters required for estimating the threshold depend on subband data. Experimental results show that the proposed thresholds remove noise significantly and preserve the edges in the scene.<\/p>\r\n","references":"[1] A.K.Katssagelous, Digital Image Restoration. New-York: springer-Verlag.1991.\r\n[2] Lakhwinder Kaur and Savita Gupta and R.C.Chauhan, \"Image denoising using wavelet thresholding,\"punjab (148106), India.2003.\r\n[3] S.ZHONG, and V.Cherkassy, \"image denoising using wavelet\r\nthresholding and model selection,\"proc.IEEE.Int.Conf.on image processing.[ICIP].Vancouver.bc, Sept. 2000.\r\n[4] Savita Gupta,\"wavelet Based Image Compression Using Daubechies\r\nFilter,\" Bombay,NCC-2002.\r\n[5] S. Grace Chang, \"adaptive Wavelet Thresholding for Image denoising\r\nand compression,\" IEEE transactions on Image processing, Vol.9, No.9\r\nseptember.2000.\r\n[6] D.L.Donoho,\"Denoising and soft thresholding,\"\r\nIEEE.transactions.information.Theory,VOL.41,PP.613-627,1995.\r\n[7] D.L.Donoho,\"nonlinear wavelet methods for recovery of signals, densities, and spectra from indirect and noisy data,\" in proc. of\r\nsymposia in applied mathematics, VOL, 00, PP.173-205, AMS, 1993.\r\n[8] S.G.Chang, B.YU, and martin Vetterli, \"spatially adaptive wavelet\r\nthresholding with context modeling for image denoising,\" IEEE.trans.on\r\nimage roc.,VOL.9,no.9,PP.1522-1531,2000.\r\n[9] S.G.Chang, B.YU, and martin Vetterli, \"bridging compression to\r\nwavelet thresholding as a denoising method,\" in proc. conf. information\r\nsciences systems, Baltimore, MD, PP.568-573, 1997.\r\n[10] D.L.Donpho, and I.M.Johnstone, \"adaptive to unknown smoothness via\r\nwavelet shrinkage,\" Journal of American statistical\r\nASSOC.,VOL.90,NO.90,PP.1200-1224,1995.\r\n[11] D.L.Donoho, and I.M.Johnstone, \"Ideal spatial adaptation via wavelet\r\nshrinkage,\"Biometrika,VOL.81,PP.425-455,1994.\r\n[12] NM.Vatterli and J.Kovacevic, Wavelets and subband Coding.\r\nEnglewood Cliffs, NJ, Prentice Hall, 1995.\r\n[13] A.K. Katssagelous and K.T.Lay, \"Maximum Likelihood Blur\r\nIdentification and Image restoration Using the EM algorithm,\" IEEE\r\nTransactions on Signal Processing, Vol. 39, No.3, March 1991.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 32, 2009"}